Deep Learning for Variational Multi-Scale Molecular Modeling

Molecular simulations are widely applied in the study of chemical and bio-physical systems. However, the
accessible timescales of atomistic simulations are limited, and extracting equilibrium properties of systems
containing rare events remains challenging. Two distinct strategies are usually adopted in this regard: either
sticking to the atomistic level and performing enhanced sampling, or trading details for speed by leveraging
coarse-grained models. Although both strategies are promising, either of them, if adopted individually,
exhibits severe limitations. In this paper we propose a machine-learning approach to ally both strategies so
that simulations on different scales can benefit mutually from their cross-talks: Accurate coarse-grained (CG)
models can be inferred from the fine-grained (FG) simulations through deep generative learning; In turn, FG
simulations can be boosted by the guidance of CG models via deep reinforcement learning. Our method
defines a variational and adaptive training objective which allows end-to-end training of parametric
molecular models using deep neural networks. Through multiple experiments, we show that our method is
efficient and flexible, and performs well on challenging chemical and bio-molecular systems.